NDLI: An adaptive feature reduction algorithm for cancer classification using wavelet decomposition of serum proteomic and DNA microarray data

Content Provider	IEEE Xplore Digital Library
Author	Rashid, S. Maruf, G.M.
Copyright Year	2011
Description	Author affiliation: Dept. of ECE, University of British Columbia, Vancouver, Canada (Rashid, S.) \|\| Dept. of CSE, United International University, Dhaka, Bangladesh (Maruf, G.M.)
Abstract	A significant challenge in DNA microarray and mass spectrometric data analysis can be attributed to the problem of having a large number of features with a small number of samples or patients in the data set. Particular care is required to deal with such a problem as the low classification accuracy of a model brought about by the small number of features may depict a low predictive capability. To overcome the associated challenges, proper approaches for data preprocessing, feature reduction and identifying the optimal set of features are critical. In this paper, a novel technique has been proposed for feature reduction and cancer classification; which is applicable for two different types of biological data. The proposed method has been implemented on Surface enhanced laser desorption/ionization time-of-flight mass spectrometric (SELDI-TOF-MS) and DNA microarray data sets. This technique is self adaptive and independent of the type data sets. We have developed a two step strategy for feature reduction such as (1) data preprocessing which includes merging and t-testing and (2) wavelet decomposition. For classification purpose, support vector machine (SVM) has been proposed. By evaluating the performance of the proposed algorithm on the two types of datasets it has been shown that the classification accuracy, sensitivity and specificity obtained by the features selected by the proposed method consistently give excellent performance.
Starting Page	305
Ending Page	312
File Size	1138380
Page Count	8
File Format	PDF
ISBN	9781457716126
e-ISBN	9781457716133
DOI	10.1109/BIBMW.2011.6112391
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Algorithm design and analysis Accuracy microarray support vector machine SELDI-TOF Proteomics t-test wavelet decomposition Kernel Least squares approximation Cancer
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A novel self-adaptive algorithm for cancer classification based on feature reduction of SELDI-TOF data using wavelet decomposition

Cancer Classification Based on the "Fingerprint" of Microarray Data

Least Squares Support Vector Machine on Moret Wavelet kernel Function

A novel voice signal discrimination algorithm and its application

Efficient sparse least squares support vector machines for pattern classification

Image Denoising Based on Least Squares Support Vector Machines

Sparse approximation using least squares support vector machines

Feature Selection and Partial Least Squares Based Dimension Reduction for Tumor Classification

Multiclass Least Squares Wavelet Support Vector Machines

An adaptive feature reduction algorithm for cancer classification using wavelet decomposition of serum proteomic and DNA microarray data

Similar Documents

A novel self-adaptive algorithm for cancer classification based on feature reduction of SELDI-TOF data using wavelet decomposition

Cancer Classification Based on the "Fingerprint" of Microarray Data

Least Squares Support Vector Machine on Moret Wavelet kernel Function

A novel voice signal discrimination algorithm and its application

Efficient sparse least squares support vector machines for pattern classification

Image Denoising Based on Least Squares Support Vector Machines

Sparse approximation using least squares support vector machines

Feature Selection and Partial Least Squares Based Dimension Reduction for Tumor Classification

Multiclass Least Squares Wavelet Support Vector Machines

An adaptive feature reduction algorithm for cancer classification using wavelet decomposition of serum proteomic and DNA microarray data